Various systems have been developed for detecting and analyzing target patterns in digital images. A digital image is a rectangular array of pixels. Each pixel is characterized by its position in the array and a plurality of numerical pixel values associated with the pixel. The pixel values represent color or grayscale information for various image layers. For example, grayscale digital images are represented by a single image layer, whereas RGB true-color images are represented by three image layers. Some existing analysis systems apply semantic networks to analyze the contents of the digital images. Systems that apply semantic networks perform object-oriented picture analysis, as opposed to solely statistical pixel-oriented analysis. Consequently, semantic network systems classify not only pixels, but also data objects linked to the pixels. The data objects that are linked to the pixels and to one another represent measurable information about the digital images.
Although object-oriented analysis can provide better results than pixel-oriented analysis alone, object-oriented analysis is also more computationally involved. Therefore, object-oriented analysis is often slower than statistical pixel-oriented analysis alone.
A method is sought that retains the advantages of object-oriented analysis, yet enhances the performance of analysis systems based on computer-implemented semantic networks. Such a method would efficiently manage the computational resources of the object-oriented analysis systems.